IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i7p2410-d340351.html
   My bibliography  Save this article

Intrinsic Cross-Correlation Analysis of Hydro-Meteorological Data in the Loess Plateau, China

Author

Listed:
  • Xiaowei Wei

    (School of Environmental Science and Engineering, Chang’an University, Xi’an 710054, China)

  • Hongbo Zhang

    (School of Environmental Science and Engineering, Chang’an University, Xi’an 710054, China
    Key Laboratory of Subsurface Hydrology and Ecological Effect in Arid Region, Ministry of Education, Chang’an University, Xi’an 710054, China)

  • Xinghui Gong

    (School of Environmental Science and Engineering, Chang’an University, Xi’an 710054, China
    Key Laboratory of Subsurface Hydrology and Ecological Effect in Arid Region, Ministry of Education, Chang’an University, Xi’an 710054, China)

  • Xingchen Wei

    (School of Environmental Science and Engineering, Chang’an University, Xi’an 710054, China)

  • Chiheng Dang

    (School of Environmental Science and Engineering, Chang’an University, Xi’an 710054, China)

  • Tong Zhi

    (School of Environmental Science and Engineering, Chang’an University, Xi’an 710054, China)

Abstract

The purpose of this study is to illustrate intrinsic correlations and their temporal evolution between hydro-meteorological elements by building three-element-composed system, including precipitation (P), runoff (R), air temperature (T), evaporation (pan evaporation, E), and sunshine duration (SD) in the Wuding River Basin (WRB) in Loess Plateau, China, and to provide regional experience to correlational research of global hydro-meteorological data. In analysis, detrended partial cross-correlation analysis (DPCCA) and temporal evolution of detrended partial-cross-correlation analysis (TDPCCA) were employed to demonstrate the intrinsic correlation, and detrended cross-correlation analysis (DCCA) coefficient was used as comparative method to serve for performance tests of DPCCA. In addition, a novel way was proposed to estimate the contribution of a variable to the change of correlation between other two variables, namely impact assessment of correlation change (IACC). The analysis results in the WRB indicated that (1) DPCCA can analyze the intrinsic correlations between two hydro-meteorological elements by removing potential influences of the relevant third one in a complex system, providing insights on interaction mechanisms among elements under changing environment; (2) the interaction among P, R, and E was most strong in all three-element-composed systems. In elements, there was an intrinsic and stable correlation between P and R, as well as E and T, not depending on time scales, while there were significant correlations on local time scales between other elements, i.e., P-E, R-E, P-T, P-SD, and E-SD, showing the correlation changed with time-scales; (3) TDPCCA drew and highlighted the intrinsic correlations at different time-scales and its dynamics characteristic between any two elements in the P-R-E system. The results of TDPCCA in the P-R-E system also demonstrate the nonstationary correlation and may give some experience for improving the data quality. When establishing a hydrological model, it is suitable to only use P, R, and E time series with significant intrinsic correlation for calibrating model. The IACC results showed that taking pan evaporation as the representation of climate change (barring P), the impacts of climate change on the non-stationary correlation of P and R was estimated quantitatively, illustrating the contribution of climate to the correlation variation was 30.9%, and that of underlying surface and direct human impact accounted for 69.1%.

Suggested Citation

  • Xiaowei Wei & Hongbo Zhang & Xinghui Gong & Xingchen Wei & Chiheng Dang & Tong Zhi, 2020. "Intrinsic Cross-Correlation Analysis of Hydro-Meteorological Data in the Loess Plateau, China," IJERPH, MDPI, vol. 17(7), pages 1-16, April.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:7:p:2410-:d:340351
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/7/2410/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/7/2410/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Furman, Edward & Zitikis, Ričardas, 2017. "Beyond The Pearson Correlation: Heavy-Tailed Risks, Weighted Gini Correlations, And A Gini-Type Weighted Insurance Pricing Model," ASTIN Bulletin, Cambridge University Press, vol. 47(3), pages 919-942, September.
    2. Aiguo Dai, 2012. "Erratum: Drought under global warming: a review," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 3(6), pages 617-617, November.
    3. Shen, Chen-hua & Li, Cao-ling, 2016. "An analysis of the intrinsic cross-correlations between API and meteorological elements using DPCCA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 446(C), pages 100-109.
    4. Lasse Koskinen, 2012. "Copula Theory and Its Applications edited by Piotr Jaworski, Fabrizio Durante, Wolfgang Härdle and Tomasz Rychlik," International Statistical Review, International Statistical Institute, vol. 80(2), pages 328-328, August.
    5. Zebende, G.F., 2011. "DCCA cross-correlation coefficient: Quantifying level of cross-correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(4), pages 614-618.
    6. Vassoler, R.T. & Zebende, G.F., 2012. "DCCA cross-correlation coefficient apply in time series of air temperature and air relative humidity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2438-2443.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lima, Cristiane Rocha Albuquerque & de Melo, Gabriel Rivas & Stosic, Borko & Stosic, Tatijana, 2019. "Cross-correlations between Brazilian biofuel and food market: Ethanol versus sugar," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 687-693.
    2. Shen, Chenhua, 2017. "A comparison of principal components using TPCA and nonstationary principal component analysis on daily air-pollutant concentration series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 453-464.
    3. Shen, Chenhua, 2019. "The influence of a scaling exponent on ρDCCA: A spatial cross-correlation pattern of precipitation records over eastern China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 579-590.
    4. Zhai, Lu-Sheng & Liu, Ruo-Yu, 2019. "Local detrended cross-correlation analysis for non-stationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 222-233.
    5. Paiva, Aureliano Sancho Souza & Rivera-Castro, Miguel Angel & Andrade, Roberto Fernandes Silva, 2018. "DCCA analysis of renewable and conventional energy prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1408-1414.
    6. Marinho, E.B.S. & Sousa, A.M.Y.R. & Andrade, R.F.S., 2013. "Using Detrended Cross-Correlation Analysis in geophysical data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2195-2201.
    7. Wang, Gang-Jin & Xie, Chi, 2013. "Cross-correlations between Renminbi and four major currencies in the Renminbi currency basket," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1418-1428.
    8. Delignières, Didier & Marmelat, Vivien, 2014. "Strong anticipation and long-range cross-correlation: Application of detrended cross-correlation analysis to human behavioral data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 47-60.
    9. Guedes, E.F. & Brito, A.A. & Oliveira Filho, F.M. & Fernandez, B.F. & de Castro, A.P.N. & da Silva Filho, A.M. & Zebende, G.F., 2018. "Statistical test for ΔρDCCA cross-correlation coefficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 134-140.
    10. Dutta, Srimonti & Ghosh, Dipak & Samanta, Shukla, 2014. "Multifractal detrended cross-correlation analysis of gold price and SENSEX," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 195-204.
    11. Ferreira, Paulo & Loures, Luís & Nunes, José & Brito, Paulo, 2018. "Are renewable energy stocks a possibility to diversify portfolios considering an environmentally friendly approach? The view of DCCA correlation coefficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 675-681.
    12. Guedes, E. & Dionísio, A. & Ferreira, P.J. & Zebende, G.F., 2017. "DCCA cross-correlation in blue-chips companies: A view of the 2008 financial crisis in the Eurozone," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 38-47.
    13. Sukpitak, Jessada & Hengpunya, Varagorn, 2016. "The influence of trading volume on market efficiency: The DCCA approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 259-265.
    14. Kristoufek, Ladislav, 2018. "Fractality in market risk structure: Dow Jones Industrial components case," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 69-75.
    15. Chen, Yingyuan & Cai, Lihui & Wang, Ruofan & Song, Zhenxi & Deng, Bin & Wang, Jiang & Yu, Haitao, 2018. "DCCA cross-correlation coefficients reveals the change of both synchronization and oscillation in EEG of Alzheimer disease patients," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 171-184.
    16. Barreto, Ikaro Daniel de Carvalho & Dore, Luiz Henrique & Stosic, Tatijana & Stosic, Borko D., 2021. "Extending DFA-based multiple linear regression inference: Application to acoustic impedance models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    17. Kristoufek, Ladislav, 2015. "Finite sample properties of power-law cross-correlations estimators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 513-525.
    18. Lin, Aijing & Shang, Pengjian & Zhong, Bo, 2014. "Hidden cross-correlation patterns in stock markets based on permutation cross-sample entropy and PCA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 259-272.
    19. Juan Manuel Julio-Román & Fredy Gamboa-Estrada, 2019. "The Exchange Rate and Oil Prices in Colombia: A High Frequency Analysis," Borradores de Economia 1091, Banco de la Republica de Colombia.
    20. Xu, Mengjia & Shang, Pengjian & Lin, Aijing, 2016. "Cross-correlation analysis of stock markets using EMD and EEMD," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 82-90.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:17:y:2020:i:7:p:2410-:d:340351. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.